International Journal of Engineering and Applied Sciences (IJEAS)

ISSN: 2394-3661, Volume-2, Issue-11, November 2015

24 www.ijeas.org

Abstract— The major purpose of this exercise is to assess the

volatility dynamics of the stock returns of the banks of India and

to determine the factor which influence and explains the stock

returns. For this exercise, the methodology GARCH (1, 1) model

is used for determining the risk factor under multi index model.

The empirical exercise suggests that in case of banking

companies stock returns are highly persistent and lagged

returns have a significant impact on the current year’s stock

returns

Index Terms— Stock returns, volatility estimation and

GARCH.

I. INTRODUCTION

Liberalization across developing nations caused widespread

exposure of different elements of risk especially to

multinational organizations. Financial Industry per say is

more venerable to such exposure and experience different risk

in their operational areas. With the economic crisis of 2008

affecting the globalized world, banks had to face the direst

consequences due to very nature of their business. All the

policy makers, regulators, academic fraternity and investors,

today are worried for the health of banks in their economies.

Banks being the nucleus center in the economy, their stock

price experience shocks due to unprecedented movements in

interest rates and exchange rates. Sensitivity in interest rate

severely affects the balance sheets of banks if the maturities of

assets and liabilities are not matched properly. Such impacts

on balance sheet can deteriorate the financial position of the

bank, causing the banks to maintain higher regulatory capital

for meeting contingencies and thereby reducing its capability

of financial intermediation. Moreover, even in situations

where bank is able to match the maturities of assets and

liabilities successfully, the devaluation in local currency will have negative consequence on the balance sheet and may lead

to default on their loans.

Unlike other emerging economies, India’s structure of

banking industry is fractured, in the sense, there exits

Government owned commercial banks, private banks, foreign

banks and cooperative banks. With such diversity in

ownership structure and existence of multi-layer structure of

banking industry in India, different categories of the banks

will have different bearing of sensitivity on their stock returns.

Our effort in this study will be to find this impact of sensitivity

in the selected factors. The empirical study will not only try to

fill the gap in the literature as this seems to be the principal

attempt to conduct such detailed joint assessment of these

Vikram Mohite, Assistant Professor, Department of International

Business Administration, College of Applied Sciences, MOHE, Nizwa,

Sultanate of Oman.

variables on Indian bank’s stock returns but also employ the

application of OLS and GARCH model for empirical analysis

in anticipation that the findings of this study will work as an

input for policy makers.

II. RESEARCH OBJECTIVE

To examine the volatility dynamics of Banks’ stock returns.

This analysis will help in identifying the interbank volatility

structure of individual banks and their effect on the banking

industry in particular and stock market in general, as well.

This analysis will not only provide the influence of past years

stock returns on the current years stock returns but also give

information on any impact created in the stock markets on

account of any significant news coming in the market. We

also intend to find the existence of persistency in the time

series data of stock returns.

III. LITERATURE REVIEW

The study of volatility in banking company’s stock returns

had been conducted with several models in past. Under the

CAPM model, [30] took interest rate as an extra market factor

in pricing securities return which is referred as Intertemporal

Capital Asset Pricing Model (ICAPM). The main finding of

his paper was investors anticipate additional compensation

for bearing the risk of change in interest rate. Also, the

implications of Arbitrage Pricing Theory (APT) provide

evidence of whether interest rate [39] or exchange rate risk

are priced factors in the equilibrium price of bank stocks. In

equilibrium, interest rate [41] and exchange rate sensitivities

exercise a significant impact on the common stocks of

financial institutions, including banks. With the liberalization

in financial market, most of the banks carry out their

operations in foreign countries and are eventually exposed to

the array of risk in recent years. More prominently, interest

rate and exchange rate changes have a significant effect on the

viability of banks because their impact cannot be eliminated

through risk management techniques [19]. The major

challenge has been for banking companies of emerging

economies as these banks are more susceptible due to paucity

of sophisticated technology and insufficiency of innovative

instruments and techniques.

The previous findings do strongly establish the sensitivity in

pricing factors on bank’s stock returns; however, there have

been very limited empirical studies. Moreover, all the

previous studies are built on the restrictive assumptions of

linearity, constant and independent variances in modeling

stock returns of banks. The reason being these studies applied

the ordinary least square (OLS) and the generalized least

square (GLS) methodologies for assessing the impact of

volatility in selected risk factors on stock returns. Until [36]

made the first attempt to apply Auto Regressive Conditional

Hetertoskedasticity (ARCH) model to quantify volatility in

Empirical study estimating volatility dynamics of

stock returns of Banks in India

Vikram Mohite

Empirical study estimating volatility dynamics of stock returns of Banks in India

25 www.ijeas.org

especially in banking sector. [17] applied the [12] GARCH

model to conclude that market risk and interest rate risk are

significant elements for non-banking finance companies,

however, for banking companies, the interest rate sensitivity

was found less significant. However, a major challenge with

their methodology emerged was the bias coefficients and lack

of consistency of the measurement factors as they are assumed

to be time invaring. Thus it is quite prevalent that in order to

build a parsimonious banking companies stock returns model

is to remove the time invariability which will make the model

more reflective and representative to capture the sensitivity in

banking companies’ stock returns.

IV. THE RESEARCH METHODOLOGY:

We intend to examine the volatility dynamics in banks stock

returns through the application of the GARCH model. To

address the issue of volatility dynamics, we aim to assess the

unexpected movement of bank stock returns to change in its

volatility. For this, we will apply [6] process of generalized

ARCH or as commonly referred as GARCH (p, q). The stock

return Yt is described in a model form as follows.

Where for some liner function (.) with Xt = (1, Yt-1,…,Yt-p),

such that has got the following properties.

Where is the information set available at time t-1.

… (2)

The above model will generate the estimates for coefficients

of which will help us to examine the volatility

dynamics of stock returns of banks for the period under the

study. The GARCH model, can be used with an exception of

three distributions viz, normal Gaussian, student-t and

generalized error distribution (GED). For this research

exercise, we assumed normal Gaussian distribution and thus

the other distribution that is student-t and GED estimations

have been left for future research quests.

V. THE DATA

The study is proposed to be conducted for a sample period of

ten years daily closing index starting from 2000 to 2010. We

propose that selection of all the banks listed in National Stock

Exchange of India (NSE). At present, there are twenty public

sector and sixteen private sector banks listed on NSE making

thirty-six banks in all. The NSE also has a bank benchmark

index called as CNX Bank Index which has the representation

of twelve banks out of the thirty-six banks. This index will

comprise the market index for the study. The daily closing

stock prices of selected banks and market index return are

available on the National Stock Exchange (NSE) website. The

returns of the banks have been calculated using the log

transformation process Yt = lnYt/lnYt-1, where Yt is the stock

price at time t and Yt-1 is the stock price at time t-1.

VI. EMPIRICAL ANALYSIS FOR VOLATILITY DYNAMICS:

The stock returns of the all the selected banks listed on

Indian National Stock Exchange (NSE) are collected and first

time series analyzed individually for all the banks. Table 1,

reveals the bank’s stock returns descriptive statistics. We

shall first explain all the dynamics of time series of daily stock

returns separately for all the banks from 2000 to 2010.

The study has coverage of thirty six banks listed on NSE, out

of which twenty banks are public sector banks and sixteen are

private sector banks. As seen from the Table 1, Public sector

banks (Figure 1) have shown majority negative average daily

stock returns for the period of study. Out of twenty banks,

only five banks have given positive mean daily returns during

the period of study. Allahabad Bank, Dena Bank, Federal

Bank, State Bank of Bikaner and Jaipur and State Bank of

Mysore, revealed positive average daily returns, of which

Allahabad Bank had the maximum daily stock return of

0.15% and State Bank of Mysore had the least daily stock

return of 0.05%. Allahabad Bank, Federal Bank and State

Bank of Mysore had negative skewness means; they had

major concentration of returns on the left side of the

distribution and had relatively few low values of returns. On

the contrary, Dena Bank and State Bank of Bikaner and Jaipur

had positive skewness means; it had right tail and had

relatively few high values. The value of kurtosis of these five

banks had been greater than three and a case of positive

excess kurtosis also termed as “leptokurtic”. Such distribution

depicts more acute peak around the mean and fatter tails.

Insert Figure 1

The residual fifteen public sector banks had negative average

daily returns for the period selected out of which UCO bank

had the least average daily returns. Seven out of fifteen banks

had a negative skewness suggesting a left tail was longer as

the mass concentration of observations was towards the right

with majority lower values. Seven of the fifteen banks had

kurtosis value near three and the rest had kurtosis value

greater than three. Thus, the primarily analysis of average

daily returns of the selected banks had been justifying and are

in-line with the stylized facts of the asset returns [7](Campbell

et al, 1997).

The private sector banks (Fig. 2) had not been different than

their public sector counterparts. They have similar dynamics

pattern for their time series of stock returns. Out of total

sixteen private sector banks, only five had a positive average

daily returns for the period of study, out of which Yes bank

had the maximum stock return of 0.20% and South Indian

Bank the least -0.09% stock returns. Analysis of skewness

shows that majority of the private sector banks returns had

negative skewness clearly showing a left tail is longer and few

lower values of returns in the total distribution of returns.

Except for Yes Bank the rest of the private sector banks had a

high value of Kurtosis, higher than three. This means the

banks which had excess Kurtosis, their returns distribution

had been peaked around its mean value and had fatter tails.

Insert Figure 2

Finally, we also analyzed the time series of CNX bank index

of NSE which comprises of twelve major banks of the

industry. The data series of ten years of the index is consistent

with the individual bank stock returns. The Negative

skewness -0.15 and excess kurtosis value 4.16, reveal that the

bank index returns distribution for ten years shows a left tailed

and leptokurtic. The average daily return had been 0. 053%

International Journal of Engineering and Applied Sciences (IJEAS)

ISSN: 2394-3661, Volume-2, Issue-11, November 2015

26 www.ijeas.org

and this return ranged between +8% and -8%. This shows that

the banking industry combined per say had a fairly low level

of volatility. However, just from the range of the maximum

and minimum return will not give us clear picture of volatility

dynamics experienced by the banking industry as a whole.

Thus, it becomes evident now to discover the volatility pattern

of the banking industry as a whole and individual bank as

well. Such analysis will not only help to determine the level of

volatility caused in the entire banking industry but also help to

identify which bank contributes majority in this volatility and

which least.

Insert Table 1

Insert Figure 3

Fig. 3 illustrates the daily close and stock returns dynamics of

CNX bank index comprising of twelve major banks of India.

The returns had been fairly consistent and rather increasing

moderately till 2004. In the year 2004 the industry

experienced a minor fall which is followed in the year 2006.

However, the difference between the fall of 2004 and 2006 is

not same. The year 2004 experienced a sudden fall in the

index on the contrary 2006 fall was gradual. This can be

inferred from the stock returns figure 4 was it is clearly seen

that in 2004 the volatility was extremely high and then

subsequently, returning to normal range of stock returns. A

major patternof volatility which immerged from the data

series is the volatility experienced in the year 2008 and

thereafter. The impact of financial crisis had left a cascading

effect on the banking industry, which resulted in fall in the

index for a long time. This effect created an environment of

uncertainty and low confidence among the traders and

investors resulting in very high level of volatility experienced

during this period. Thus we will now make a deliberate

attempt to analyze the volatility dynamics of the banking

industry stock returns.

The parameter estimates for GARCH (1, 1) model is

presented in the following Table 2. We have estimated ω, α

and β the three important parameters of the model stated in

equation 2 for all the banks individually and the market as a

whole. The parameter ω gives the impact of any news or event

which caused sudden turmoil in the market causing the

variance in stock returns drastically, the next parameter, β is

the ARCH parameters which finds the impact of lagged

squared error term on the current period variance of stock

returns and the last parameter α is the GARCH parameter

which assesses the impact of lagged period variance on the

current period variance.

The analysis of all the banks and the market index has

discovered that all the three parameters estimated are

statistically significant at assumed 1% level of significance.

The value of ω, which is constant volatility, for all the banks

and the index is very close to zero which suggests that the

constant variance had negligible impact on volatility of the

stock returns of the banks. The GARCH (Beta) and the ARCH

(Alpha) coefficients for all the banks are also very statistically

significant at 1% assumed level of significance. This implies

that lagged residuals square and the lagged variance have a

significant impact on the current variance of the stock returns.

Moreover, the sum of two coefficients GARCH (Beta) and the

ARCH (Alpha) is very close to one. This gives a very clear

understanding of high persistence in the variance. This

persistency illustrates that every fall in stock returns is

followed by fall and every rise is followed by rise.

Insert Table 2

Insert Figure 4

Fig. 4, shows the conditional variance of nine banks together.

It is observed that Andhra Bank(AnB), Bank of India (BOI),

Canara Bank (CanB) and Corporation Bank (CorpB) had

experienced a very high level of volatility in their returns and

Allahabad Bank (AllB), Bank of Baroda (BOB), Bank of

Maharashtra (BOM), Dena Bank (DB) and Federal Bank

(FedB) had relatively low sensitivities in their stock returns,

however, all the nine banks had high volatility during the two

important period sighted earlier the fall of 2004 and the

financial crisis of 2008. Financial crisis did not caused

volatility in the returns of all the banks. Referring to the

conditional volatility of the above nine banks, it can be

inferred that BOB and BOM had a relatively low impact of

crisis on their returns.

Insert Figure 5

Fig. 5 gives the conditional variance of eight of the banks

selected for the study. Here in figure 6 we can observe that

Indian Overseas Bank (IOB), Oriental Bank of Commerce

(OBC), Punjab National Bank (PNB), State Bank of India

(SBI) and Union Bank of India (UBI) had major volatility

pattern and the rest have relatively low volatility pattern.

These banks are big banks compared to others who are

relatively smaller ones and hence, they had lover volatility in

their conditional variance. PNB, OBC and IDBI had shown

increased volatility at the beginning of the period and the

others had increased volatility at the end of the period of

study. SBI, UBI and IOB had been the major banks which

experienced increased volatility at regular intervals

suggesting these bank’s stock returns had been highly volatile

during the last decade.

Insert Figure 6

Fig. 6 gives the relative comparison of conditional variance of

other nine banks, this time these bank are private sectors

banks. Looking to the conditional variance of these nine

private sector banks, it is evident that Karnataka Bank (KB),

City Union Bank (CUB) and HDFC Bank (HDFC) had

experienced a low level of activity causing low volatility on

their counters. HDFC Bank is one of the biggest bank of the

India, and had relatively low volatility among others clearly

reveals that the performance of this bank had been consistent

make its stock returns fairly consistent as well on a daily basis.

However, Axis Bank (AxB), Development Credit Bank

(DCB), Dhanlakshmi Bank (DhanB), ICICI Bank (ICICI),

IndusInd Bank (IndB) and Jammu and Kashmir Bank (J&KB)

had similar high level of volatility patterns as observed in

other banks.

Insert Figure 8

Fig. 8 gives the conditional variance of the last set of six banks

selected for this exercise. KarurVysa Bank (KVB), Kotak

Bank (KB), Lakshmi Vilas Bank (LVB), South Indian Bank

(SIB) had seen a low volatility and only few spikes, giving

understanding that these banks had only few occasions were

volatility had spurted and then returned to its normal

condition. However, that was not the case for Vijaya Bank

(VB) and Yes Bank (YB), who had fairly high level of

volatility on their counters. VB as seen in the figure had

continues patterns of high spikes making this bank stock

returns highly volatile. YB also had an active counter with

increased volatility observed during later period during the

decade as seen in the figure 7.

Empirical study estimating volatility dynamics of stock returns of Banks in India

27 www.ijeas.org

VII. DIAGNOSTIC TESTING

It is been seen that size of the bank is not a factor which

influenced the volatility on their stock returns. Some big size

banks had quite calm volatility patterns and few small banks

had a very active volatility patterns. However, this model of

volatility has needs to be tested using the diagnostic testing.

Thus, our next task is to check the validity of this GARCH

model, which we propose by testing, weather the residuals are

auto-correlated, normally distributed and existence of ARCH

effect. For testing on these three stages, we propose the

following null hypothesis and alternate hypothesis.

Null HypothesisH0 (1): The residuals from the model are not

serially correlated

Alternate Hypothesis H1(1): The residuals from the model are

serially correlated.

Null Hypothesis H0(2): Residuals are normally distributed.

Alternate Hypothesis H1(2): Residuals are not normally

distributed.

Null HypothesisH0 (3): ARCH effect does not exists in the

residuals.

Alternate HypothesisH1 (3): ARCH effect does exists in the

residuals.

To test the first null hypothesis on serial correlation (1), we

calculated correlograms of the autocorrelation and partial

autocorrelation of the squared residuals with thirty lags and

also computed the Ljung-Box Q-statistics for the each

corresponding lag and there p-values. We also conducted the

ARCH LM (3) test of heteroskedasticity. It is observed from

the tests conducted on all the banks individually and on the

market index, that only UBI, AxB, KB and SIB are the banks

were the two null hypothesis of residuals not serially

auto-correlated and non-existence of ARCH effect are

rejected at 5% significance level as their corresponding

p-values generated from the two tests are less than 5% and for

all the rest of the banks and CNX bank Index, the null

hypothesis is not rejected. As the null hypothesis of these four

banks is rejected their conditional variance contains ARCH or

GARCH errors. However, in case of other banks, such errors

do not exist in conditional variance. Hence, the remaining

bank’s conditional variance is comparatively giving a more

realistic measure of volatility dynamics. This shows that

residuals from the GRACH model with normal distribution

for the majority of the banks are not serially correlated and

does not contain the issue of heteroscidasticity. The detailed

summary of diagnostic test results is presented in the

following Fig. 9. Finally, we conducted the last test on

residuals of GARCH model of normality. For conducting

normality test we used the Jarque-Bera statistics and its

corresponding p-value. The test result reveals that none of the

banks had residuals normally distributed as all the banks

Jarque-Bera statistics p-value are less than 5% statistical

significance level. Thus, for the last test, we reject the null

hypothesis (2) and derive the result that the model’s residuals

are not normally distributed. Any model, to be used for

forecasting requires the three diagnostic test null hypotheses

being not rejected to make the model’s coefficients consistent

and efficient. If the model’s coefficients pass the diagnostic

test, then these coefficients can be used for out of sample

forecasting and such forecast will generate more accurate

results for decision making for future. However, due to the

inherent issue present in the economic time series as discussed

earlier, to get residuals normally distributed is very difficult.

Hence, this model can be implemented for forecasting with a

paramount assumption that residuals are normally distributed.

By giving such assumption, the model coefficient can be used

consistently for forward planning and decision making for

future through a near to accurate forecast.

VIII. CONCLUSION

The current research is structured in three broad categories. In

the first segment, we analyzed the banks stock returns using

the GARCH (1, 1) model. The purpose of application of

GARCH was to assess the volatility dynamics of the

individual bank’s stock returns for the period under the study

which will help in understanding the variance. According to

our findings, the stock returns variance equations coefficients

are highly significant. The estimation of the model, suggested

that the stock returns are highly persistent. Out of the total

thirty six banks undertaken for the research, only three banks

had statistically insignificant coefficients. However, it has

also been observed that certain significant shocks had

cascading impact in the time series. The analysis of

conditional variance has given this common impact on the

time series of majority of the banks. The crisis period had

major impact on the volatility of the stock returns of the

banks. The stock returns, showed high degree of variances

and these were consistently followed in the subsequent years.

Hence, the findings showed that banks which were highly

traded and were listed long time on the stock exchanges were

the banks who observed high volatility and their returns

experienced the impact of news in the market. On a broad

classification, public sector banks had experienced major

shifts in volatility compared to their private sector

counterpart. This finding is made on the basis of observation

made to the conditional volatility of all the banks. Moreover,

the ARCH and the GARCH coefficients were highly

significant and their summation was unity, this finding

suggests that the returns data is highly persistent.

APPENDIX

FIGURE 1:

PUBLIC SECTOR BANKS AVERAGE DAILY

RETURNS

International Journal of Engineering and Applied Sciences (IJEAS)

ISSN: 2394-3661, Volume-2, Issue-11, November 2015

28 www.ijeas.org

FIGURE 2:

PRIVATE SECTOR BANKS AVERAGE DAILY

RETURNS

FIGURE 3:

CNX BANK INDEX DAILY CLOSE & RETURNS

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

02 04 06 08 10

-.16

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

02 04 06 08 10

FIGURE 4: CONDITIONAL VOLATILITY

.000

.002

.004

.006

.008

.010

.012

03 04 05 06 07 08 09 10

Allahabad Bank Conditional variance

.000

.002

.004

.006

.008

.010

.012

.014

250 500 750 1000 1250 1500 1750 2000 2250 2500

Andhra Bank Conditional variance

.000

.002

.004

.006

.008

.010

250 500 750 1000 1250 1500 1750 2000 2250 2500

Bank of India Conditional variance

.000

.004

.008

.012

.016

.020

04 05 06 07 08 09 10

Bank of Maharashtra Conditional variance

Corporation Bank Conditional variance Dena Bank Conditional variance

.000

.004

.008

.012

.016

.020

02 04 06 08 10

Bank of Baroda Conditional variance

.000

.001

.002

.003

.004

.005

.006

.007

03 04 05 06 07 08 09 10

Canara Bank Conditional variance

Federal Bank Conditional variance

FIGURE 5: CONDITIONAL VOLATILITY

IDBI Bank Conditional variance Indian Overseas Bank Conditional variance

Punjab National Bank Conditional variance State Bank of Bikaner and Jaipur Conditional variance

State Bank of Mysore Conditional variance Union Bank of India Conditional variance

Oriental Bank of Commerce Conditional variance State Bank of India Conditional variance

FIGURE 6: CONDITIONAL VOLATILITY

Axis Bank Conditional variance City Union Bank Conditional variance

Empirical study estimating volatility dynamics of stock returns of Banks in India

29 www.ijeas.org

Dhanlaxmi Bank Conditional variance HDFC Bank Conditional variance

Indusind Bank Conditional variance

Jammu and Kashmir Bank Conditional variance

Developmnent Credit Bank Conditional variance ICICI Bank Conditional variance

Karnataka Bank Conditional variance

FIGURE 7: CONDITIONAL VOLATILITY

Karur Vysa Bank Conditional variance Kotak Bank Conditional variance

South Indian Bank Conditional variance Vijaya Bank Conditional variance

Lakshmi Vilas Bank Conditional variance YES Bank Conditional variance

TABLE 1: DESCRIPTIVE STATISTICS

Name of Banks Mean Standard Minimu Maxim Skewness Kurtosis

Allahabad Bank 0.154% 0.02875 -19% 17% -0.02836 4.78607

Andhra Bank -0.109% 0.02900 -16% 20% 0.16878 4.43700

Bank of Baroda -0.118% 0.03052 -18% 24% 0.07392 5.31195

Bank of India -0.140% 0.03314 -20% 23% 0.06456 3.69850

Bank of Maharashtra -0.035% 0.02805 -18% 17% -0.48235 7.42803

Canara Bank -0.097% 0.02999 -15% 17% 0.08013 3.15946

Corporation Bank -0.082% 0.02807 -16% 19% -0.27022 4.65131

Dena Bank 0.097% 0.03315 -24% 24% 0.22631 5.62006

Federal Bank 0.123% 0.03046 -25% 18% -0.06853 7.49551

IDBI Bank -0.070% 0.03442 -18% 23% -0.18857 5.16985

Indian Overseas Bank -0.112% 0.03007 -18% 22% 0.15661 4.48848

Oriental Bank of Commerce -0.114% 0.02869 -19% 19% -0.07116 3.38042

Punjab National Bank -0.173% 0.02916 -16% 20% -0.04522 3.22192

State Bank of Bikaner and Jaipur 0.111% 0.02419 -7% 18% 1.74692 12.02150

State Bank of India -0.107% 0.02465 -18% 15% 0.09110 3.99949

State Bank of Mysore 0.053% 0.02023 -11% 13% -0.60502 9.86219

State Bank of Travencore -0.104% 0.02379 -8% 10% 0.00874 1.90994

Syndicate Bank -0.124% 0.02698 -13% 16% 0.79543 5.37124

UCO Bank -0.204% 0.02872 -15% 11% -0.32956 2.97590

UNION Bank of India -0.150% 0.02504 -14% 12% -0.02496 3.12702

Axis Bank -0.179% 0.03308 -19% 22% -0.28222 4.85419

City Union Bank -0.118% 0.02766 -18% 17% -0.37162 6.17788

Development Credit Bank 0.017% 0.04108 -20% 21% 0.45820 3.48152

Dhanlaxmi Bank -0.031% 0.03234 -11% 18% 1.52475 7.48791

HDFC Bank 0.095% 0.02272 -23% 23% 0.19201 11.07330

ICICI Bank 0.080% 0.03093 -22% 21% -0.07687 5.07274

Indusind Bank -0.102% 0.03488 -16% 19% -0.07625 3.61036

ING Vysa Bank -0.059% 0.02859 -18% 12% -0.71940 4.25040

JAMMU & KASHMIR Bank -0.116% 0.02645 -18% 15% -0.62981 5.49809

Karnataka Bank -0.097% 0.02979 -19% 14% -0.64286 5.22656

Karur Vysa Bank -0.119% 0.02359 -16% 15% -0.71216 6.41284

Kotak Bank -0.189% 0.03213 -18% 17% -0.22762 3.31454

Lakshmi Vilas Bank -0.094% 0.03003 -18% 19% -0.83097 8.15378

South Indian Bank -0.097% 0.03003 -19% 21% -0.39452 5.34378

Vijaya Bank 0.021% 0.02563 -9% 18% 1.04072 8.38605

YES Bank 0.208% 0.02579 -9% 12% 0.74185 2.32897

CNX Bank Index 0.053% 0.01625 -8% 8% -0.15453 4.16097

Private Sector Banks

Public Sector Banks

TABLE 2: PARAMETERS ESTIMATES OF

BANKING STOCK RETURNS VOLATILITY

Name of

Banks ω β α

Public Sector Banks

Allahabad Bank 0.0000511*** 0.155225*** 0.786747***

Andhra Bank 0.0000431*** 0.228325*** 0.740148***

Bank of Baroda 0.0000313*** 0.13958*** 0.836012***

Bank of India 0.0000865*** 0.161887*** 0.765326***

Bank of

Maharashtra 0.000301*** 0.35073*** 0.316857***

Canara Bank 0.0000407*** 0.102851*** 0.860927***

Corporation

Bank 0.0000486*** 0.140672*** 0.807252***

Dena Bank 0.000248*** 0.276182*** 0.508862***

Federal Bank 0.00011*** 0.176769*** 0.710944***

IDBI Bank 0.000436*** 0.213839*** 0.442547***

Indian Overseas

Bank 0.0000348*** 0.128581*** 0.837377***

Oriental Bank

of Commerce 0.0000141*** 0.113569*** 0.880496***

Punjab National

Bank 0.0000127*** 0.116796*** 0.873834***

State Bank of

Bikaner and

Jaipur 0.000137*** 0.417869*** 0.370329***

State Bank of

India 0.0000125*** 0.090094*** 0.89174***

State Bank of

Mysore 0.000103*** 0.27162*** 0.550655***

State Bank of

Travencore 0.000131*** 0.467055*** 0.346663***

Syndicate Bank 0.0000404*** 0.161952*** 0.799659***

UCO Bank 0.0000616*** 0.098512*** 0.835147***

UNION Bank

of India 0.0000502*** 0.157134*** 0.796461***

Private Sector Banks

Axis Bank 0.0000267*** 0.099157*** 0.880028***

City Union 0.000293*** 1.94214*** 0.071848***

International Journal of Engineering and Applied Sciences (IJEAS)

ISSN: 2394-3661, Volume-2, Issue-11, November 2015

30 www.ijeas.org

Bank

Development

Credit Bank 0.0000379*** 0.073691*** 0.904101***

Dhanlaxmi

Bank 0.000131*** 0.157046*** 0.741351***

HDFC Bank 0.0000419*** 0.226906*** 0.709895***

ICICI Bank 0.0000285*** 0.119556*** 0.84908***

Indusind Bank 0.0000738*** 0.13045*** 0.813251***

ING Vysa Bank 0.00005*** 0.070118*** 0.870809***

JAMMU &

KASHMIR

Bank 0.0000363*** 0.115589*** 0.83994***

Karnataka

Bank 0.000352*** 0.730943*** 0.262698***

KarurVysa

Bank 0.000259*** 1.88332*** 0.104021***

Kotak Bank 0.000495*** 0.720846*** 0.145629***

Lakshmi Vilas

Bank 0.0000807*** 0.368856*** 0.677147***

South Indian

Bank 0.000232*** 0.473851*** 0.414568***

Vijaya Bank 0.0000771*** 0.115276*** 0.797671***

YES Bank 0.0000366*** 0.126585*** 0.842395***

CNX Bank

Index 0.00000836*** 0.106991*** 0.877829***

Note: *, **, *** Statistically significant at 10%, 5% and 1% significance level

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Vikram Mohite is currently working as Assistant Professor – Accounting

and Finance at the College of Applied Sciences, Nizwa. He shares diversified

experiences in the field of academic, corporate and training. He is also a

certified corporate trainer and had been awarded for his achievements. His

primary research interests are in the area of Asset Pricing, Empirical

Finance, Financial Intermediation, Portfolio Analysis and Investments. His

research work has been accepted at various academic conferences and has a

good number of publications to his credit. He is life member of Indian

Accounting Association and India Econometric Society (TIES). He has a

PhD from the reputed University of Pune, India and post-graduation and

graduation from the prestigious The Maharaja Sayajirao University of

Baroda, India. He has also done his MS from the Adam Smith Business

School, University of Glasgow, UK in Quantitative Finance.